Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.7 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
view(gapminder)
The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
…
We see an interesting spread with an outlier to the right. Answer the following questions, please:
data_1952 <- gapminder %>%
filter(year == 1952)
min(data_1952$gdpPercap)
## [1] 298.8462
max(data_1952$gdpPercap)
## [1] 108382.4
richest <- match(c(max(data_1952$gdpPercap)), data_1952$gdpPercap)
richest
## [1] 72
data_1952$country[richest]
## [1] Kuwait
## 142 Levels: Afghanistan Albania Algeria Angola Argentina Australia ... Zimbabwe
Why does it make sense to have a log10 scale on x axis? > Because the numbers differ a lot (the smallest being 298.8462 and the biggest being 108382.4). The plot would be very difficult to read if we would try to visualise this.
Who is the outlier (the richest country in 1952 - far right on x axis)? > Kuwait
Next, you can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
…
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Tasks:
# fixing the scientific notation
options(scipen=999)
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point(aes(colour = factor(continent))) +
labs (x = "GDP per Capita", y = "Life Expectancy") +
scale_x_log10()
data_2007 <- gapminder %>%
filter(year == 2007)
top_5 <- tail(sort(data_2007$gdpPercap),5)
top_5
## [1] 40676.00 42951.65 47143.18 47306.99 49357.19
top_5_richest <- match(c(top_5), data_2007$gdpPercap)
top_5_richest
## [1] 63 135 114 72 96
data_2007$country[top_5_richest]
## [1] Ireland United States Singapore Kuwait Norway
## 142 Levels: Afghanistan Albania Algeria Angola Argentina Australia ... Zimbabwe
Ireland, United States, Singapore, Kuwait and Norway
The comparison would be easier if we had the two graphs together,
animated. We have a lovely tool in R to do this: the
gganimate package. Beware that there may be other packages
your operating system needs in order to glue interim images into an
animation or video. Read the messages when installing the package.
Also, there are two ways of animating the gapminder ggplot.
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
…
This plot collates all the points across time. The next step is to
split it into years and animate it. This may take some time, depending
on the processing power of your computer (and other things you are
asking it to do). Beware that the animation might appear in the bottom
right ‘Viewer’ pane, not in this rmd preview. You need to
knit the document to get the visual inside an html
file.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
…
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Now, choose one of the animation options and get it to work. You may
need to troubleshoot your installation of gganimate and
other packages
transition_states() and transition_time()
functions respectively)anim3 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year) +
labs(title = 'Year: {frame_time}')
anim3
anim4 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year) +
labs(title = 'Year: {frame_time}',
x = "GDP per Capita",
y = "Life Expectancy")
anim4
gapminder_unfiltered dataset and
download more at https://www.gapminder.org/data/ ]How did the European countries’ GDP and life expectancy were in 1962 compared to the Americas?
data_1962 <- gapminder %>%
filter(year == 1962 & continent=="Europe" | year == 1962 & continent=="Americas")
gdp_1962 <- ggplot(data_1962, aes(gdpPercap, lifeExp, size = pop)) +
geom_point(aes(colour = factor(continent))) +
scale_x_log10() # convert x to log scale
gdp_1962
> On this graph, we can see that Europe had a better GDP status and
life expectancy overall than America, who has more scattered
datapoints.